We use interferometric synthetic aperture radar observations to investigate the fault geometry and afterslip evolution within 3 years after a mainshock. The postseismic observations favor a ramp‐flat structure in which the flat angle should be lower than 10°. The postseismic deformation is dominated by afterslip, while the viscoelastic response is negligible. A multisegment, stress‐driven afterslip model (hereafter called the SA‐2 model) with depth‐varying frictional properties better explains the spatiotemporal evolution of the postseismic deformation than a two‐segment, stress‐driven afterslip model (hereafter called the SA‐1 model). Although the SA‐2 model does not improve the misfit significantly, this multisegment fault with depth‐varying friction is more physically plausible given the depth‐varying mechanical stratigraphy in the region. Compared to the kinematic afterslip model, the mechanical afterslip models with friction variation tend to underestimate early postseismic deformation to the west, which may indicate more complex fault friction than we expected. Both the kinematic and stress‐driven models can resolve downdip afterslip, although it could be affected by data noise and model resolution. The transition depth of the sedimentary cover basement interface inferred by afterslip models is ∼12 km in the seismogenic zone, which coincides with the regional stratigraphic profile. Because the coseismic rupture propagated along a basement‐involved fault while the postseismic slip may activate the frontal structures and/or shallower detachments in the sedimentary cover, the 2017 Sarpol‐e Zahab earthquake may have acted as a typical event that contributed to both thick‐ and thin‐skinned shortening of the Zagros in both seismic and aseismic ways.

In this study, Sentinel-1 and Advanced Land Observation Satellite-2 (ALOS-2) interferometric synthetic aperture radar (InSAR) and global positioning system (GPS) data were used to jointly determine the source parameters and fault slip distribution of the Mw 6.6 Hokkaido eastern Iburi, Japan, earthquake that occurred on 5 September 2018. The coseismic deformation map obtained from the ascending and descending Sentinel-1 and ALOS-2 InSAR data and GPS data is consistent with a thrust faulting event. A comparison between the InSAR-observed and GPS-projected line-of-sight (LOS) deformation suggests that descending Sentinel-1 track T046D, descending ALOS-2 track P018D, and ascending ALOS-2 track P112A and GPS data can be used to invert for the source parameters. The results of a nonlinear inversion show that the seismogenic fault is a blind NNW-trending (strike angle ~347.2°), east-dipping (dip angle ~79.6°) thrust fault. On the basis of the optimal fault geometry model, the fault slip distribution jointly inverted from the three datasets reveals that a significant slip area extends 30 km along the strike and 25 km in the downdip direction, and the peak slip magnitude can approach 0.53 m at a depth of 15.5 km. The estimated geodetic moment magnitude released by the distributed slip model is $6.16\times {10}^{18}\mathrm{N}·\mathrm{m}$ , equivalent to an event magnitude of Mw 6.50, which is slightly smaller than the estimates of focal mechanism solutions. According to the Coulomb stress change at the surrounding faults, more attention should be paid to potential earthquake disasters in this region in the near future. In consideration of the possibility of multi-fault rupture and complexity of regional geologic framework, the refined distributed slip and seismogenic mechanism of this deep reverse faulting should be investigated with multi-disciplinary (e.g., geodetic, seismic, and geological) data in further studies.

We use interferometric synthetic aperture radar (InSAR) observations to investigate the fault model and afterslip evolution within 3 years after the 2017 Sarpol-e Zahab, Iran, Mw 7.3 earthquake. The anti-listric fault which is very similar to flat-and-ramp structure inverted by kinematic afterslip models is proposed to simulate the coseismic slip and afterslip evolution. Compared with listric faults, linear inversions demonstrate that a planar fault can explain coseismic deformation well enough. However, the stress perturbations caused by this basement-involved faulting propagated upward to the sedimentary cover. The transition of sedimentary cover-basement interface inferred by afterslip models is at the depth of ~13 km in the seismogenic zone, which coincides with the regional stratigraphic profile and indicates that the significant afterslip updip of the coseismic rupture is mainly controlled by frictional property. We additionally find the postseismic deformation is dominated by afterslip while the viscoelastic response is negligible with the best-fitting viscosity which is on the order of 10 ¹⁹ Pa s. Compared to the best-fitting kinematic afterslip model, the stress-driven afterslip model tends to underestimate early postseismic deformation to the west, which may indicate the spatial heterogeneity of the frictional property of fault plane. Because the coseismic rupture propagated along a basement-involved fault while the postseismic slip was likely to activate the frontal structures and/or shallower detachments in the sedimentary cover, the 2017 Sarpol-e Zahab earthquake may act as a typical event which contributes to both of the thick- and thin-skinned shortening of the Zagros in both seismic and aseismic way.

SUMMARY: On 2015 November 4, an Mw 6.5 earthquake struck the east region of Alor Island, eastern Indonesia. Here, we use Sentinel-1 and ALOS-2 data to explore the coseismic surface displacement of this earthquake. Based on the ALOS-2 coseismic interferograms, the first fault model and coseismic slip distribution of the 2015 Alor earthquake are presented in this study. The preferred slip model links to a blind south-southeast striking, south-southwest dipping strike-slip fault with amount of normal slip component and a peak slip of 2.09 m at 2.34 km of depth. Considering the fact that the ascending and descending InSAR prediction of the distributed slip model does not fit the InSAR observations at the northwest and southeast tips with the simple planar fault model. We tried to construct a strike-variable fault model to further improve data fit. The results of the calculated Coulomb stress change imply that the regions with positive CFS changes mainly located at the northwest and southeast extremities of the rupture of the Alor earthquake, and in the lobes north and south of the rupture. The most striking discovery from the InSAR observations of the Alor earthquake is that most of the displacements occurred on a fault whose existence was unknown before the earthquake.

Global earth's surface is subject to adverse effects of a variety of slow and sustained geological hazards, such as land subsidence, earthquake, tectonic motion, mining, landslides, coastal erosion, volcano and permafrost, caused by natural forces and anthropogenic activities disturbance. Earth observation data enable scientists to efficiently assess ground deformation and damages posed by these hazards and result in significant resilience planning. Therefore, preserving a complete record of past, present, and future surface movements is essential for disaster risk mitigation and property protection. It is widely acknowledged that interferometric synthetic aperture radar (InSAR) is a highly effective and widely used geodetic technique for understanding the spatiotemporal evolution of historical ground surface deformation. Meanwhile, deformation prediction is crucial for preventing and mitigating geological hazards, considering the long revisit cycle of satellites and the time it takes to process data.In this study, we propose a strategy that predicts spatiotemporal InSAR time series combining independent component analysis (ICA) and Long Short-Term Memory (LSTM) machine learning model, Here ICA, as a blind signal separation method, is deployed to identify and capture the InSAR displacement signals of interest and characterize each independent time series signal, caused by various natural or anthropogenic processes. In addition, considering that ignoring heterogeneity would reduce the model's accuracy, K-means clustering approach is jointly used to divide the study area into several spatiotemporal homogeneity subregions over a large-scale region, where we assume that the points in the same cluster have similar spatiotemporal behavior. Finally, neural network models for each cluster are constructed and optimal parameters are determined. The suggested study framework is used into two real datasets with diverse deformation characteristics: land subsidence post-seismic time series. The results reveal that our suggested ICA-assisted LSTM model outperforms the original LSTM, with the average prediction accuracy for one-step prediction improved by 34% and 17%, respectively. Furthermore, we mapped the spatiotemporal predicted results of subsidence and post-seismic events in 60 days and examine their performance and limitations, the results of which show high consistency using the enhanced processing technique.The successful prediction on subsidence and post-seismic deformation further indicates that the proposed prediction strategy can be applied to monitor other large-scale geo-hazards with sustained and slow deformation for rapid decision-making and timely risk mitigation. Furthermore, the proposed prediction methodology is applicable to different scenarios of derivative applications. It has application potential in deformation time series fusion across a specified region over the last 20 years, as well as automatic cataloging for abnormal time identification of post-events over broad areas, such as landslides and volcano eruption events, by deviating from the original time series.